(numpy.ndarray) : scaling vector. Note: .. math:: ascendant :\\frac{1}{nfilts}\\times [ 1, ..., i, ..., nfilts] descendant :\\frac{1}{nfilts}\\times [ nfilts, ..., i, ..., 1] """ return{ "ascendant":np.array([i/nfiltsforiinrange(1,nfilts+1)]).reshape( ...
import numpy as np from numpy import loadtxt, zeros, ones, array, linspace, logspace from pylab import scatter, show, title, xlabel, ylabel, plot, contour #Load the dataset data = loadtxt('housing.data',dtype=np.float) #Plot the data scatter(data[:, 5], data[:, 13], marker='o'...
Let’s assume that our goal is to reduce the dimensions of add-dimensional dataset by projecting it onto a(k)(k)-dimensional subspace (wherek<dk<d). So, how do we know what size we should choose forkk(kk= the number of dimensions of the new feature subspace), and how do we know ...